P.033 Experiences with epilepsy treatments: a qualitative content analysis of online patient support group discussions
Bibliographic record
Abstract
Background: To promote patient-centred care in epilepsy, it is essential to understand the issues most important to patients. Literature on patient perceptions of epilepsy treatments is sparse. One source of data is online patient support groups. Patients turn to social media for support from other patients and often express viewpoints not shared with healthcare providers. Methods: Using a qualitative content analysis approach, we analyzed major online epilepsy patient support groups. We initially selected a month-long discussion text across these forums, and further threads were sampled with maximum variation until theme saturation was reached. For data coding and analysis, we employed a combination of a priori codes and emergent codes, using NVivo 11 for data analysis. Results: In our preliminary analysis, we identified topics and categorized them into themes: 1) differential perceptions and understandings of epilepsy; (2) understanding treatment options; (3) experiences of physiological and psychological treatment side effects; (4) concerns about healthcare providers’ knowledge and communication regarding treatments. Conclusions: Preliminary results indicate a variety of patient perceptions and understandings of epilepsy and its treatments. Our findings also suggest that patient educational needs should be addressed by incorporating their understanding and concerns. Shared-decision making tools informed by patient perceptions could help healthcare providers better communicate treatment options with patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".